On-vehicle image processing apparatus

ABSTRACT

An on-vehicle image processing apparatus includes: an image taking apparatus for taking an image of a forward view of a vehicle; an edge detection section for generating a detection image data based on the image data; and a feature point detection section for detecting at least one feature point based on the detection image data. The feature point detection section categorizes the at least one feature point into a lane division line feature point, a branch feature point, and a dead end and obstacle feature point. The detection image data includes a lane division line detection scanning region set in a near region from the vehicle, a branch detection scanning region set in a middle region from the vehicle, and a dead end and obstacle detection scanning region set in a far region from the vehicle. The amount of scanning process can be reduced.

INCORPORATION BY REFERENCE

This patent application is based on Japanese Patent Application No.2007-192277. The disclosure of the Japanese Patent Application isincorporated herein by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an image processing apparatus installedon a vehicle and is designed to take an image of a forward view of thevehicle.

2. Description of Related Art

Image processing apparatuses which are installed on vehicles have beendeveloped. Such apparatuses can be applied to automatic drivingtechniques. As for an image processing apparatus, the following threecharacteristics are required.

Firstly, in order to recognize the lane on which the vehicle travels, itis necessary to take an image of the front view of the vehicle by acamera such as the CCD (Charge Coupled Device) or the CMOS(Complementary Metal Oxide Semiconductor) image sensor and performprocessing of the image in real time.

Secondly, a correct recognition of the lane is required for the safedriving. Therefore, the input image data of the lane is required to havea feature that brings about less error in the image recognition.

Thirdly, it is required to extract information of edges of the lanemarking drawn on the surface of the lane in the front view image. Insuch information, the position of a lane division line on the lanesurface, the existence or absence of a branch of a lane, or theexistence or absence of a dead end or an obstacle is represented, forexample.

Here, some examples of image processing apparatuses for vehicles areintroduced.

Japanese Laid Open Patent Application JP-P 2003-308534A describes awhite line recognition apparatus. In this white line recognitionapparatus, a forward road surface image including a running lane isimaged, and the road surface image is laterally scanned. By detecting anup-edge where the intensity (namely, brightness, concentration or valueof a pixel) of the image is relatively increased by more than apredetermined value and a down-edge where the intensity of the image isrelatively decreased by more than a predetermined value, a brightportion caused by a white line on a road surface is extracted. At thistime, white line position candidate points which represent side edgeportion of the lane is determined for each longitudinal position of theroad image. In this case, when only one pair edge composed of an up-edgeand a down-edge that correspond to the bright portion is detected at thesame position in the longitudinal direction of the road surface image, apoint having a predetermined relative position preset for the pair edgeis defined as the white line position candidate point. Also, when aplurality of pair edges are detected, in accordance with the array ofthe plurality of pair edges, a point having a predetermined relativeposition preset for the plurality of pair edges is defined as the whiteline position candidate point. In this way, in the white linerecognition apparatus, even in the road portion in which the pluralityof white lines are arrayed, a white line position candidate point whenthe white line of the road portion is assumed to be the single roadportion is obtained.

Also, Japanese Patent Publication No. 2712809 describes a vehicleobstacle detection apparatus. In this vehicle obstacle detectionapparatus, only a horizontal edge and a vertical edge which extendlonger as they exist in the lower portion of a screen from an inputimage are extracted, and an area surrounded by the horizontal andvertical edges are recognized as an obstacle. In this construction, adistance measuring system can be constructed by using only one camera.

SUMMARY

However, in on-vehicle image processing apparatuses (in particular,white line recognizing apparatuses) exemplified above, for detecting thelane division line, the branch, the dead end and the obstacle, fixedtemplates for detecting them are prepared and the entire region of theimage is scanned for checking the matching of the image to thetemplates. In this case, since the entire region is scanned, there is aproblem that the number of the processing steps of scanning becomeslarge, correspondingly to the frame size of the image.

In a first aspect of the present invention, an on-vehicle imageprocessing apparatus includes: an image taking apparatus configured togenerate image data by taking an image of a forward view of a vehicle;an edge detection section configured to generate a detection image dataincluding pixels of m rows and n columns (m and n are integers more than2) based on the image data; and a feature point detection sectionconfigured to detect at least one feature point based on the detectionimage data and output the at least one feature point to an outputapparatus. The feature point detection section categorizes the at leastone feature point into a lane division line feature point indicating apoint on a lane division line, a branch feature point indicating abranch of a lane, and a dead end and obstacle feature point indicating adead end of a lane or an obstacle on a lane. The detection image dataincludes a lane division line detection scanning region set fordetecting the lane division line feature point, a branch detectionscanning region set for detecting the branch feature point, and a deadend and obstacle detection scanning region set for detecting the deadend and obstacle feature point. The distance between a vehicle loadingthe image taking apparatus and a position on a lane corresponding to apixel in the detection image data becomes shorter from a first row to am-th row of the detection image data. The dead end and obstacledetection scanning region is set in pixels positioned from the first rowto a m1-th row of the detection image data, the branch detectionscanning region is set in pixels positioned from the m1-th row to am2-th row of the detection image data, and the lane division linedetection scanning region is set in pixels positioned from the m2-th rowto the m-th row of the detection image data, wherein 1<m1<m2<m.

As mentioned above, in an on-vehicle image processing apparatus (M100)according to an embodiment of the present invention, a scanning regiondedicated to each of the feature points (a lane division line, a branch,a dead end or an obstacle) of the road is set for a detection image data(image data for detecting feature points). Thus, the number of the stepsof image scanning process can be reduced.

Also, in an on-vehicle image processing apparatus (M100) according to anembodiment of the present invention, the characteristic of the image,namely, the perspective in which the near side appears large and the farside appears small is used for determining the scanning regionsrespectively correspond to the types of targets on a road to bedetected. Thus, it is possible to reduce the influence of noise in thescanning region, so that it is possible to reduce errors in thedetection of feature points on the road.

Also, in an on-vehicle image processing apparatus (M100) according to anembodiment of the present invention, a lane division line detectionscanning region (A101) is set at the nearest side (the lower side of theimage region). Thus, even when the vehicle position is deviated from thelane or when the vehicle runs through a curve, the lane division linecan be imaged at least partially. Hence, it becomes easy to carry outthe process for calculating the vehicle position even in such cases.

Also, in an on-vehicle image processing apparatus (M100) according to anembodiment of the present invention, the far portion in the image is setas the dead end and obstacle detection scanning region. Therefore, theexistence or absence of a dead end or an obstacle can be detectedearlier than the detection of the branch. Thus, it becomes easy to carryout the judgment, such as a speed control, a direction control and thelike, when the output apparatus (M200) controls the driving of thevehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, advantages and features of the presentinvention will be more apparent from the following description ofcertain preferred embodiments taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 shows a configuration of an on-vehicle image processingapparatus;

FIG. 2A is a flowchart showing an operation of an on-vehicle imageprocessing apparatus;

FIG. 2B is a flowchart showing an operation of an on-vehicle imageprocessing apparatus;

FIG. 2C is a flowchart showing an operation of an on-vehicle imageprocessing apparatus;

FIG. 2D is a flowchart showing an operation of an on-vehicle imageprocessing apparatus;

FIG. 3A is a view showing a two-dimensional space filter (smoothingfilter) used in an operation of an on-vehicle image processingapparatus;

FIG. 3B is a view showing a two-dimensional space filter (Laplaciansmoothing filter) used in an operation of an on-vehicle image processingapparatus;

FIG. 4A is a view describing a detection of a lane division line featurepoint, as an operation of an on-vehicle image processing apparatus;

FIG. 4B is a view describing a detection of a lane division line featurepoint, as an operation of an on-vehicle image processing apparatus;

FIG. 5 is a view describing an operation of an on-vehicle imageprocessing apparatus, showing a lane division line detection scanningregion A101, a branch detection scanning region A102 and a dead end andobstacle detection scanning region A103 of a feature point detectingimage data;

FIG. 6 is a view describing a calculation of a current vehicle position,as an operation of an on-vehicle image processing apparatus;

FIG. 7A is a view describing an effect when the step S112 is executed;

FIG. 7B is a view describing an effect when the step S112 is executed;and

FIG. 7C is a view describing an effect when the step S112 is executed.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, an on-vehicle image processing apparatus according toembodiments of the present invention will be described with reference tothe attached drawings.

[Configuration]

FIG. 1 shows a configuration of an on-vehicle image processing apparatusM100 according to an embodiment of the present invention. The on-vehicleimage processing apparatus M100 is installed on a vehicle together withthe other equipment using the result of image processing like anautomatic driving system M200. The on-vehicle image processing apparatuscontains an imaging apparatus M101, an analog to digital (A/D) converterM102 and an image processing apparatus M110.

The imaging apparatus M101 has a camera, such as the CCD or CMOS imagesensor, and is set in the forward view of the vehicle. The A/D converterM102 is connected to the imaging apparatus M101 and the image processingapparatus M110. The image processing apparatus M110 is a computer, andis realized by hardware exemplified by an electronic circuit andsoftware exemplified by a computer program.

The image processing apparatus M110 contains a memory M103, an edgedetection section M104, an edge correction and noise reduction sectionM105 and a feature point detection section M111. The feature pointdetection section M111 contains a lane division line detection sectionM106, a branch detection section M107, a dead end and obstacle detectionsection M108 and a vehicle position estimation section M109. The memoryM103 is connected to the A/D converter M102 and the edge detectionsection M104. The edge detection section M104 is connected to the edgecorrection and noise reduction section M105. The edge correction andnoise reduction section M105 is connected to the lane division linedetection section M106, the branch detection section M107 and the deadend and obstacle detection section M108. The lane division linedetection section M106 is connected to the vehicle position estimationsection M109. The vehicle position estimation section M109, the branchdetection section M107 and the dead end and obstacle detection sectionM108 are connected to an output apparatus like an automatic drivingsystem M200.

[Operation]

FIGS. 2A to 2D are flowcharts showing operations of the on-vehicle imageprocessing apparatus M100 shown in FIG. 1.

The imaging apparatus M101 takes an image of a forward view of thevehicle on a road by using a camera and generates an analog image datarepresenting the image ahead of the vehicle from the vehicle. The A/Dconverter M102 performs an analog to digital conversion on the analogimage data to generate digital image data and stores the digital imagedata in the memory M103 (Step S101). This digital image data includespixel data corresponding to pixels arranged in a matrix of m rows and ncolumns, which represent the forward view from the vehicle. Here, m andn are integers of 3 or more.

The edge detection section M104 generates an two-dimensional spacefiltered image data by performing a two-dimensional space filteringprocess on the m rows and n columns pixels of the digital image datastored in the memory M103.

In the “two-dimensional space filter,” the filtering process applied toa focused pixel is performed based on the information of a certain twodimensional region surrounding the focused pixel on the image. In thisembodiment, in the two-dimensional space filtering process, a square ora rectangular surrounding (circumference) region is set to each of thepixels g(i, j). Then, a weight is preliminary set to each of the pixelsin the surrounding region for each of the pixels. The weighted averageof the intensity of the surrounding region is calculated as the filtered(recalculated or average) intensity of the center pixel f(i, j) for eachof the pixels in the entire images. For example, in a case that thesurrounding region is set to be a 3×3 square region, the averageintensity f(i, j) is calculated by averaging the intensities of the 9pixels g(i−1, j−1), g(i, j−1), g(i+1, i−1), g(i−1, j), g(i, j), g(i+1,j), g(i−1, j+1), g(i, j+1) and g(i+1, j+1) with their respectiveweights. When the image is a color image, this averaging is performedfor each of the color components of the pixels for each of the m rowsand n columns pixels of the digital image data.

Here, when i and j are integers that satisfy 2≦i≦=(m−1) and 2≦j≦(n−1),respectively, the average intensity f(i, j) is represented by thefollowing equation (1).

$\begin{matrix}{{f( {i,j} )} = {\sum\limits_{k = {- 1}}^{1}\;{\sum\limits_{l = {- 1}}^{1}{{g( {{i + k},{j + l}} )} \times {M( {i,j} )}_{kl}}}}} & (1)\end{matrix}$

Here, M(i, j) is a space filter coefficient matrix which functions as atable of weights in the circumference region for each of the pixels, andthe suffixes indicate the position of each element of this matrix. Thematrix M is represented by the following equation (2).

$\begin{matrix}{{M( {i,j} )} = \begin{pmatrix}{m( {{i - 1},{j - 1}} )} & {m( {i,{j - 1}} )} & {m( {{i + 1},{j - 1}} )} \\{m( {{i - 1},j} )} & {m( {i,j} )} & {m( {{i + 1},j} )} \\{m( {{i - 1},{j + 1}} )} & {m( {i,{j + 1}} )} & {m( {{i + 1},{j + 1}} )}\end{pmatrix}} & (2)\end{matrix}$

By setting various weights in the space filter coefficient matrix M(i,j), various kinds of the filtering can be achieved. As suchtwo-dimensional space filtering processes, the smoothing filteringprocess (Step S102), and the Laplacian filtering process (also called asLaplacian smoothing filtering process) (Step S103) in which the 3×3 maskfilters shown in FIGS. 3A, 3B are respectively used are exemplified.

At first, in the smoothing filtering process (also called as simplesmoothing filtering process), the average intensity f(i, j) iscalculated by:f(i,j)={1×g(i−1,j−1)+1×g(i,j−1)+1×g(i+1,j−1)+1×g(i−1,j)+1×g(i,j)+1×g(i+1,j)+1×g(i−1,j+1)+1×g(i,j+1)+1×g(i+1,j+1)}/9.

The coefficients located before each value g(x, y) is represented by thefollowing equation (3) in the form of the space filter coefficientmatrix M(i, j).

$\begin{matrix}{{M( {i,j} )} = {\begin{pmatrix}1 & 1 & 1 \\1 & 1 & 1 \\1 & 1 & 1\end{pmatrix} \times \frac{1}{9}}} & (3)\end{matrix}$

Next, the Laplacian filtering process determines the difference betweenthe intensity f(i, j) represented by the focused pixel g(i, j) and theintensities f(i−1, j−1), f(i, j−1), f(i+1, j−1), f(i−1, j), f(i+1, j),f(i−1, j+1), f(i, j+1) and f(i+1, j+1) that are represented by the 8surrounding pixels g(i−1, j−1), g(i, j−1), g(i+1, j−1), g(i−1, j),g(i+1, j), g(i−1, j+1), g(i, j+1) and g(i+1, j+1), respectively, whichare the pixels around the focused pixel. At this time, the space filtercoefficient matrix M(i, j) is represented by the equation (4).

$\begin{matrix}{{M( {i,j} )} = \begin{pmatrix}{- 1} & {- 1} & {- 1} \\{- 1} & 8 & {- 1} \\{- 1} & {- 1} & {- 1}\end{pmatrix}} & (4)\end{matrix}$

The edge detection section M104 performs a binarization process on the mrows and n columns pixels of the two-dimensional space filtered imagedata to generate the binarized image data (Step S104).

In this binarization process, the first value and the second value, forexample “1” and “0,” constituting a binary digit is set. One of “1” and“0” is set to indicate the binary number corresponding to the black whenthe calculated intensity of each of the m rows and n columns pixels ofthe two-dimensional space filtered image data is below a predeterminedthreshold value, and other of “1” and “0” is set to indicate the binarynumber corresponding to the white when the calculated intensity of eachof them is above the predetermined threshold value. As a result, thebinarized image data composed of pixels each of which has either “1” or“0” value is generated. The edge detection section M104 outputs thebinarized image data to the edge correction and noise reduction sectionM105 as a noise detection image data.

The edge correction and noise reduction section M105 generates a featurepoint detection image data by performing an edge correction and noisereduction on the m rows and n columns pixels of the noise detectionimage data (Step S105).

In this edge correction and noise reduction process, each of the numbersof the binary numbers “0” and “1” represented by the surrounding pixelsaround the focused pixel are counted for each of the m rows and ncolumns pixels of the noise detection image data, respectively. Andthen, the binary number of each of the pixels is replaced by a newbinary number in accordance with the number obtained by counting “0” and“1” around the pixel, the proportion of the number of “0” pixels to the“1” pixels, and the matching result of the arrangement of the “0” and“1” pixels with a preset pattern. This process is performed on allpixels.

As such a condition, the following three items can be exemplified.

Condition 1: When the binary number represented by the focused pixel isthe black “1” and when the 4 pixels or more among the surrounding pixelshave the binary number representing the white “0”, the binary numberrepresented by the focused pixel is replaced from the black “1” to thewhite “0”.

Condition 2: When the binary number represented by the focused pixel isthe white “0” or the black “1” and when one, two or three pixels amongthe surrounding pixels have the binary number representing the white“0”, the binary number of the focused pixel is kept in the currentvalue.

Condition 3: When the binary number represented by the focused pixel isthe white “0” or the black “1” and when all of the surrounding pixelshave the binary number representing the black “1”, the binary numberrepresented by the focused pixel is replaced with the black “1”.

The foregoing condition is one example, and other kind of condition canbe used to the edge correction and noise reduction process.

As for the feature point detection image data obtained by the processesat the steps S102 to S105, its contrast is enhanced and the noise issuppressed as compared with the digital image data obtained at the stepS101. Since the contrast is enhanced, the edge is enhanced, whichenables the feature point on the road to be easily extracted. The edgecorrection and noise reduction section M105 outputs the feature pointdetection image data to the feature point detection section M111.

The feature point detection section M111 scans the m rows and n columnspixels of the feature point detection image data by one row at a time asa scanning line, and detects the pixels having the binary number “1”representing the black from the m rows and n columns pixels, and detectsfeature points of the lane in accordance with the pixels representingthe black “1.” For example, in the case of the white line, as shown inFIG. 4A, the pixel representing the black “1” among the m rows and ncolumns pixels of the feature point detection image data represents awhite line.

The feature point is categorized into a plurality of types, exemplifiedby: a lane division line feature point representing a line painted on aroad surface for dividing adjacent lanes, typically a white line; abranch feature point representing a branch of the road; and a dead endand obstacle feature point representing that the road leads to a deadend or the road is blocked by an obstacle.

In the explanation in embodiments of the present invention, instead ofthe lane division line which is typically a white line, other kinds oflane marking can be processed in a same manner. Such lane markingincludes a yellow line representing that the vehicle lane is in ano-overtaking zone, and the characters representing a traffic rule suchas the legal speed, a path direction and the like.

The feature point detection section M111 scans the m rows and n columnspixels of the feature point detection image data by one row at a time.At this process, the pixels from the (n/2)-th column to the first columnare scanned in this order, and reversely, the pixels from the (n/2)-thcolumn to the n-th column are scanned in this order, as shown in FIGS.4A and 4B. Along this scanning path, when the binary value of thescanned pixel is changed from the white “0” represented by a first pixelgroup to the black “1” represented by a second pixel group, the boundarybetween the first pixel group “0” and the second pixel group “1” isreferred to as an up-edge. Similarly, when the binary value is changedfrom the black “1” represented by the second pixel group to the white“0” represented by a third pixel group along the scanning path, theboundary between the second pixel group “1” and the third pixel group“0” is referred to as a down-edge.

The lane division line detection section M106, the branch detectionsection M107 and the dead end and obstacle detection section M108 in thefeature point detection section M111 include a fixed template for a lanedivision line detection (not shown), a fixed template for a branchdetection (not shown) and a fixed template for a dead end and obstacledetection (not shown), respectively. The lane division line detectionsection M106 detects a lane division line based on the preset lanedivision line feature point pattern represented by the fixed templatefor lane division line detection. The branch detection section M107detects a branch based on the preset branch feature point patternrepresented by the fixed template for branch detection. And the dead endand obstacle detection section M11 detects dead end and obstacle basedon the preset dead end and obstacle feature point pattern represented bythe fixed template for dead end and obstacle detection. Each of thepreset lane division line feature point pattern, the preset branchfeature point pattern and the preset dead end and obstacle feature pointpattern is composed of a pair of the up-edge and the down-edge.

As shown in FIG. 5, the feature point detection image data includes alane division line detection scanning region A101, a branch detectionscanning region A102 and a dead end and obstacle detection scanningregion A103. In the m rows and n columns pixels of the feature pointdetection image data, the distance between a position on a lane shown inthe image and the vehicle corresponds to the longitudinal position ofthat position on the image. The distance between the vehicle and aposition on a lane corresponding to a pixel in the image becomes shorterfrom the first row to the m-th row of the image. Namely, the distancebetween the vehicle and the position having smaller k on the image islarger than the distance between the vehicle and the position havinglarger k on the image, when the value k is counted up from the top (k=1)to the bottom (k=m) of the image. Also, the relation 1<m1<m2<m isassumed. In this case, the dead end and obstacle detection scanningregion A103 corresponds to a far region from the vehicle and set in thepixels from the first row to the m1-th row, among the m rows and ncolumns pixels of the feature point detection image data. The branchdetection scanning region A102 correspond to the middle region from thevehicle and set in pixels from the (m1+1)-th row to the m2-th row, andthe lane division line detection scanning region A101 correspond to thenear region from the vehicle and set in pixels from the (m2+1)-th row tothe m-th row.

The lane division line detection section M106 scans the pixels from the(m2+1)-th row to the m-th row in the lane division line detectionscanning region A101, by one row at a time, from the central position ofthe image in the left direction. That is, the left side pixels from the(n/2)-th column to the first column are scanned in this order. At thistime, the lane division line detection section M106 detects the pixel inwhich the binary number represents the black “1”, as a candidate lanedivision line feature point, among the pixels from the (m2+1)-th row tothe m-th row. And a pattern matching is performed for the candidate lanedivision line feature point by using the fixed template for a lanedivision line detection. In the pattern matching, when the candidatelane division line feature point “1” matches with the preset lanedivision line feature point pattern represented by the fixed templatefor a lane division line detection, the lane division line detectionsection M106 judges the candidate lane division line feature point as aleft side lane division line feature point (Step S106).

The lane division line detection section M106 scans the pixels from the(m2+1)-th row to the m-th row in the lane division line detectionscanning region A101, by one row at a time, from the central position ofthe image in the right direction. That is, the right side pixels fromthe (n/2)-th column to the n-th column are scanned in this order. Atthis time, the lane division line detection section M106 detects thepixel in which the binary number represents the black “1”, as acandidate lane division line feature point, among the pixels from the(m2+1)-th row to the m-th row. And a pattern matching is performed forthe candidate lane division line feature point by using the fixedtemplate for a lane division line detection. In the pattern matching,when the candidate lane division line feature point “1” matches with thepreset lane division line feature point pattern represented by the fixedtemplate for a lane division line detection, the lane division linedetection section M106 judges the candidate lane division line featurepoint as a right side lane division line feature point (Step S107).

The lane division line detection section M106 scans the pixels in thelane division line detection scanning region A101 by one row at a time,and carries out an complementation process for complementing the portionin which the lane division line feature point is not be detected (StepS112). This complementation is carried out as follows. When the lanedivision line detection section M106 scans the pixels in the lanedivision line detection scanning region A101 by one row at a time, it isassumed that, a lane division line feature point is detected in one ofthe left side and the right side of the lane division line detectionscanning region A101, and is not detected in other one of them. In thiscase, in the right side or left side from which the lane division linefeature point is not detected, the central pixel of the right or lefthalf region on the lateral scanning line is complemented as acomplemented lane division line feature point for complementing alacking point of a lane division line.

For example, when the lane division line detection section M106 scansthe pixels in the lane division line detection scanning region A101 byone row at a time, the right side lane division line feature point isassumed to be detected, and the left side lane division line featurepoint is assumed not to be detected. In this case, from the left pixelsfrom which the left side lane division line feature point cannot bedetected, the pixel on the (n/2)-th column being the central pixel iscomplemented as the complemented lane division line feature point.Similarly, when the lane division line detection section M106 scans thepixels in the lane division line detection scanning region A101 by onerow at a time, the left side lane division line feature point is assumedto be detected, and the right side lane division line feature point isassumed not to be detected. In this case, from the right pixels fromwhich the right side lane division line feature point cannot bedetected, the pixel on the (n/2)-th column being the central pixel iscomplemented as the complemented lane division line feature point. Bythis complementation process, the points positioned on right or leftlane division line but lacking in the image is complemented.

The lane division line detection section M106 sends the lane divisionline feature points (the left side lane division line feature point andthe right side lane division line feature point), (the left side lanedivision line feature point and the complemented right side lanedivision line feature point) and (the right side lane division linefeature point and the complemented left side lane division line featurepoint), when the pixels in the lane division line detection scanningregion A101 are scanned by one row at a time, to the vehicle positionestimation section M109.

The vehicle position estimation section M109 calculates the vehicleposition, which is considered to indicate the position of the vehicle onthe lane, as a feature point on the lane, in accordance with the lanedivision line feature points (the left side lane division line featurepoint and the right side lane division line feature point), (the leftside lane division line feature point and the complemented right sidelane division line feature point) and (the right side lane division linefeature point and the complemented left side lane division line featurepoint) which are detected by one row at a time by the lane division linedetection section M106.

Specifically, as shown in FIG. 6, the coordination of the lane divisionline detection scanning region A101 is replaced so that the coordinatevalue of the pixel of the (m2+1)-th row first column are defined as (0,0), and the coordinate value of the pixel of the m-th row n-th column isdefined as (Xmax, Ymax). Also, the lateral coordinate value of the pixelcorresponding to the left side lane division line feature point orcomplemented left side lane division line feature point is defined asline_L, and the lateral coordinate value of the pixel corresponding tothe right side lane division line feature point or complemented rightside lane division line feature point is defined as line_R. Namely, asshown in FIG. 6, the Line_L indicates the lane division line featurepoint detected by scanning left side of image, and the Line_R indicatesthe lane division line feature point detected by scanning right side ofimage.

When the current vehicle position on one scanning line is assumed to becenter_pos, the vehicle position estimation section M109 calculates thecurrent vehicle position center_pos by the following equation:center_pos=line_(—) L+{(line_(—) R−line_(—) L)/2}

Next, the coordinate value (0, Xmax/2) and (Ymax, Xmax/2) respectivelyrepresent the pixels of the (m2+1)-th row (n/2)-th column and the m-throws (n/2)-th column are defined as the central position of the image onone scanning line. Then, defining the central position as CENTER, andthe deviation amount from the central position CENTER with respect tothe vehicle position center_pos as line_delta, the vehicle positionestimation section M109 calculates the deviation amount line_delta fromthe central position CENTER of the image on one scanning line by thefollowing equation:line_delta=CENTER−center_pos

This calculation is performed on every scanning line between the(m2+1)-th and m-th rows in the lane division line detection scanningregion A101 (Step S113).

Next, the vehicle position estimation section M109 calculates theaverage value of the deviation amounts line_delta from the centralposition CENTER that are determined for all of the scanning linesbetween the (m2+1)-th and m-th rows and determines the current vehicleposition in accordance with the average value (Step S114).

Specifically, defining the total value of the deviation amountsline_delta from the central position CENTER with regard to all the linesbetween the (m2+1)-th and m-th rows as Σline_delta and the average valueof the deviation amounts line_delta as frame_delta, the vehicle positionestimation section M109 calculates an average value frame_delta by thefollowing equation:frame_delta=(Σline_delta)/Ymax.

Further, the vehicle position estimation section M109 calculates thecurrent vehicle running position Current_CENTER indicating the currentposition of the vehicle by the following equation:Current_CENTER=CENTER−frame_delta.

Here, if Current_CENTER>0, it is indicated that the vehicle is in theright side from the central position of the image, and ifCurrent_CENTER<0, it is indicated that the vehicle is in the right sidefrom the central position of the image.

Also, at the step S114, the vehicle position estimation section M109outputs the calculated current vehicle position as the running positioninformation to the automatic driving system M200. Or, the currentvehicle position and the lane division line feature points (the rightside lane division line feature point and the left side lane divisionline feature point) may be outputted as the running position informationto the automatic driving system M200.

The branch detection section M107 scans the pixels from the (m1+1)-throw to the m2-th row in the branch detection scanning region A102, byone row at a time, from the central position of the image in the leftdirection. That is, the pixels from the (n/2)-th column to the firstcolumn are scanned in this order. At this time, the branch detectionsection M107 detects the pixel whose binary number represents the black“1”, among the pixels from the (m1+1)-th row to the m2-th row, as acandidate branch feature point. The branch detection section M107performs a pattern matching of the detected candidate branch featurepoint by using the fixed template for the branch detection. In thepattern matching, if the candidate branch feature point “1” matches withthe preset branch feature point pattern represented by the fixedtemplate for the branch detection, the branch detection section M107judges the candidate branch feature point as a left branch feature point(Step S108).

The branch detection section M107 scans the pixels from the (m1+1)-throw to the m2-th row as the branch detection scanning region A102, byone row at a time, from the central position of the image in the rightdirection. That is, the pixels from the (n/2)-th column to the n-thcolumn are scanned in this order. At this time, the branch detectionsection M107 detects the pixel whose binary number represents the black“1”, among the pixels from the (m1+1)-th row to the m2-th row, as acandidate branch feature point. The branch detection section M107performs a pattern matching of the detected candidate branch featurepoint by using the fixed template for the branch detection. In thepattern matching, if the candidate branch feature point “1” matches withthe preset branch feature point pattern represented by the fixedtemplate for the branch detection, the branch detection section M107judges the candidate branch feature point as a right branch featurepoint (Step S109).

The branch detection section M107 generates branch information, whichindicates whether the obtained branch feature point (the right branchfeature point or the left branch feature point) exists on the right sidefrom the central position of the image, or exits on the left side, orexits on both the right and left sides, or does not exist on both theright and left sides, and then outputs the branch information to theautomatic driving system M200 (Step S115).

The dead end and obstacle detection section M108 scans the pixels fromthe first row to the m1-th row in the dead end and obstacle detectionscanning region A103, by one row at a time, from the central position ofthe image in the left direction. That is, the pixels from the (n/2)-thcolumn to the first column are scanned in this order. At this time, thedead end and obstacle detection section M108 detects the pixel whosebinary number represents the black “1” as a candidate dead end andobstacle feature point, among the pixels from the first row to the m1-throw. The dead end and obstacle detection section M108 performs a patternmatching of the detected candidate dead end and obstacle feature pointby using the fixed template for the dead end and obstacle detection. Inthe pattern matching, if the candidate dead end and obstacle featurepoint “1” matches with the preset dead end and obstacle feature pointpattern represented by the fixed template for the dead end and obstacledetection, the dead end and obstacle detection section M108 judges thecandidate dead end and obstacle feature point as a left dead end andobstacle feature point (Step S110).

The dead end and obstacle detection section M108 scans the pixels fromthe first row to the m1-th row as the dead end and obstacle detectionscanning region A103, by one row at a time, from the central position ofthe image in the right direction. That is, the pixels from the (n/2)-thcolumn to the n-th column are scanned in this order. At this time, thedead end and obstacle detection section M108 detects the pixel whosebinary number represents the black “1” as a candidate dead end andobstacle feature point, among the pixels from the first row to the m1-throw. The dead end and obstacle detection section M108 performs a patternmatching of the detected candidate dead end and obstacle feature pointby using the fixed template for the dead end and obstacle detection. Inthe pattern matching, if the candidate dead end and obstacle featurepoint “1” matches with the preset dead end and obstacle feature pointpattern represented by the fixed template for the dead end and obstacledetection, the dead end and obstacle detection section M108 judges thecandidate dead end and obstacle feature point as a right dead end andobstacle feature point (Step S111).

The dead end and obstacle detection section M108 generates dead end andobstacle information and outputs it to the automatic driving system M200(Step S116). The dead end and obstacle information indicates whether theobtained dead end and obstacle feature point (the right dead end andobstacle feature point and the left dead end and obstacle feature point)exists on the right side from the central position of the image, orexits on the left side, or exits on both the right and left sides, ordoes not exist on both the right and left sides.

[Comparison]

Here, an on-vehicle image processing apparatus M100 according to anembodiment of the present invention and a referential example of anon-vehicle image processing apparatus (a lane marking recognizingapparatus and a vehicle obstacle detecting apparatus) are compared.

[Comparison 1]

In an on-vehicle image processing apparatus M100 according to anembodiment of the present invention, scanning regions each of which isdedicated to any of the types of feature points on a road (a lanedivision line, a branch, a dead end and an obstacle) are set for thedetection image data (the feature point detection image data). Thus, theamount of the scanning process can be reduced compared with a case inwhich whole image is scanned for all types of feature points.

In an on-vehicle image processing apparatus in according to areferential example (particular, a lane marking recognition apparatus),as the process for detecting patterns like a lane marking, a branch, adead end and an obstacle, the entire region of the image are scannedwith performing the matching of the scanned pixels and fixed templateseach of which is corresponding to the patterns. In this case, the entireregion is scanned, which results in a problem that the amount of theprocesses when the scanning is carried out becomes large,correspondingly to the frame size of the image.

On the other hand, in the on-vehicle image processing apparatus M100according to an embodiment of the present invention, the whole area ofthe detection image data is divided into the lane division linedetection scanning region A101, the branch detection scanning regionA102 and the dead end and obstacle detection scanning region A103. Then,the lane division line detection section M106, the branch detectionsection M107 and the dead end and obstacle detection section M108 scanthe pixels in the lane division line detection scanning region A101, thebranch detection scanning region A102 and the dead end and obstacledetection scanning region A103, by one row at a time, respectively. Inthis way, in the on-vehicle image processing apparatus M100 of thisembodiment, a detection image data is divided into the lane divisionline detection scanning region A101, the branch detection scanningregion A102 and the dead end and obstacle detection scanning regionA103. Thus, the amount of the information processed in the scanning canbe reduced.

Also, the distance between the vehicle and the point corresponding tothe m rows and n columns pixels of the detection image data becomesshorter from the first row to the m-th row, in this order. Also,1<m1<m2<m is assumed. In this case, the dead end and obstacle detectionscanning region A103, the branch detection scanning region A102 and thelane division line detection scanning region A101 correspond to thepixels from the first row to the m1-th row, the pixels from the(m1+1)-th row to the m2-th row, and the pixels from the (m2+1)-th row tothe m-th row, among the m rows and n columns pixels of the detectionimage data, respectively. Consequently, there are the following merits.

Firstly, the feature of the image (the perspective of the image, namely,the near side appears large and the far side appears small) is used tospecify the scanning region based on the target object to be detected.Thus, in the scanning region, the influence of noises can be suppressedand erroneous detection of feature points can be reduced.

Secondly, the lane division line detection scanning region A101 is setat the nearest side (the lower side region of the image). Thus, evenwhen the vehicle is largely deviated from the center of a lane, or runson a curve, at least a part of a lane division line can be imaged.Hence, it is easy to carry out the process for calculating the vehicleposition.

Thirdly, the region far from the vehicle is scanned for detecting thedead end and obstacle, so that the existence or absence of a dead end oran obstacle can be recognized earlier than the existence or absence of abranch. Thus, when the automatic driving system M200 controls the motionof the vehicle in response to the detected feature points, it becomeseasy to carry out the judgment for controlling the vehicle movement inresponse to the existence or absence of a dead end or an obstacle.

In this way, in the on-vehicle image processing apparatus M100 of thisembodiment, because of the arrangement of the dedicated scanningregions, the influence of noise can be suppressed and erroneousdetection of feature points can be reduced. Thus, the amount of theinformation processed in scanning can be reduced. For example, when thescanning region is defined as ⅓ of the entire image, the amount of theinformation to be processed is also ⅓.

[Comparison 2]

In an on-vehicle image processing apparatus M100 of this embodiment, fora left side pixel or a right side pixel positioned on a lane divisionline but is not detected, the pixel positioned in the center of the lefthalf or right half region is complemented as the complemented lanedivision line feature point. Thus, the vehicle position currentlyestimated can be made close to the true vehicle compared with a case inwhich the complementation is not carried out.

In a referential example of an on-vehicle image processing apparatuswhich does not have a complementation function, when a vehicle isrunning, a lane division line extending from lower left to upper rightpositioned in the left side of the vehicle and another extending fromlower right to upper left positioned in the right side of the vehicleare imaged. The current vehicle position is recognized based on theamount of disposition from the center of the image in a lateraldirection obtained in accordance with the positions of the left andright lane division lines which are calculated by matching a pair of theedge of the right lane division line and the left lane division line toa fixed template. However, in the case when the vehicle position iswidely deviated from the lane or when the vehicle is running a curve,only one of the right and left lane division lines is imaged. As aresult, a pair edge of the left and right lane division lines is notdetected in the matching using the fixed template, so that the vehicleposition cannot be calculated.

Here, in the referential example of an on-vehicle image processingapparatus without the complementation function (in particular, a lanemarking recognizing apparatus), tentative lane division line position isdefined based on the position of the pair edge composed of the up-edgeand the down-edge. However, unless an extrapolating circuit fordetecting a pair edge by scanning in further right and left directionfrom the image field is installed, the position of the lane divisionlines cannot be obtained. In an automatic driving, this disables therunning. On the other hand, the installation of a complementationcircuit results in a problem that the circuit scale is increased.

On the other hand, according to an on-vehicle image processing apparatusM100 of an embodiment of the present invention, a complementationprocess is carried out as below. When scanning the pixels on the lanedivision line detection scanning region A101 are scanned by one row at atime, it is assumed that only one of the left side and right side lanedivision line feature points is detected and the other is not detected.In the following, the left or right half region in which the other lanedivision line feature point being not detected is positioned therein isreferred to as the feature point lacking half region. The feature pointlacking half region is either the left half region composed of thepixels from the (n/2)-th column to the first column or the right halfregion, and the right half region composed of the pixels from the(n/2)-th column to the n-th column. In this case, the center position inthe lateral direction in the feature point lacking half region isdefined as the complemented lane division line feature point of the halfregion.

Then, the position of the vehicle on the road is estimated in accordancewith one lane division line feature point in a half region and thecomplemented lane division line feature point in the other half region.In this way, in the on-vehicle image processing apparatus M100 of thisembodiment, in the feature point lacking half region, the complementedlane division line feature point is complemented in the feature pointlacking half region. As a result, the deviation of the estimated vehicleposition from the true vehicle position can be reduced. Consequently,there are following merits.

Firstly, as shown in FIG. 7A, for the image in which an edge is lost,the deviation is reduced. The arrow P1 indicates that in the row in thehalf region, an edge is detected. The dotted arrow P2 indicates that inthe row in the half region, an edge is not detected. The circle P3indicates the current vehicle position calculated from detected edges.The black circle P4 indicates the current vehicle value roughlyestimated from a detected edge and an edge obtained by thecomplementation process for complementing the lacking lane division linefeature point.

Secondly, as shown in FIG. 7B and FIG. 7C, even when a vehicle isrunning through a curve, or when a vehicle is largely deviated from thecenter of a lane, in an automatic driving, by calculating the truevehicle position based on the complemented edge, the automatic drivingin a course closer to a desired course can be achieved.

For example, in FIG. 7B, an image in the case that the vehicle isrunning in a curve is shown. In FIG. 7C, an image in the case that thevehicle is largely deviated from the center of a lane is shown. In thesecases, the vehicle is positioned in the right side of the lane and thedriving to control the vehicle to the left direction is desired.However, if the complementation explained above is not performed, thevehicle position is judged to be in a left side of the lane. In theon-vehicle image processing apparatus M100 in this embodiment, althoughthere is a slight deviation from the true vehicle position, it ispossible to judge the vehicle position to be in the right region of thelane.

In this way, the on-vehicle image processing apparatus M100 in thisembodiment has a wide allowable range for noises, the deviation of thevehicle position and the deviation of the installed position of thecamera. Thus, the vehicle position can be calculated by using an edgedetection based on a fixed template without adding an extra circuit oran extra process.

[Comparison 3]

In an on-vehicle image processing apparatus M100 of an embodiment of thepresent invention, the number of circuits for attaining a filteringprocess for the edge detection can be reduced. Thus, the circuit scalecan be reduced.

In a referential example of an on-vehicle image processing apparatus (inparticular, in an apparatus for detecting an obstacle on a lane), twokinds of one dimensional filtering processes are executed, namely, thehorizontal (lateral) and the vertical (longitudinal) edge detectionfiltering processes. Thus, in this referential technique, the circuitfor attaining the one-dimensional space filtering processes of the twokinds is required, so that there is a problem that the circuit scale isincreased.

On the other hand, in the on-vehicle image processing apparatus M100 ofan embodiment of the present invention, the filtering process can beattained by using only one two-dimensional space filtering process. Thatis, it can be attained only by adding a circuit for attaining onetwo-dimensional space filtering process. Thus, the circuit scale can bereduced compared with the referential example.

Also, in an embodiment of the present invention, the two-dimensionalspace filtering process can be executed on the basis of only theintensities of the focused pixel and the surrounding 8 pixels.Therefore, in order to attain the two-dimensional space filteringprocess, it is enough to prepare only 3 line buffers, which is in thehalf size of the referential example. For example, when the number ofthe line buffers required in the referential example of the on-vehicleimage processing apparatus is 6, the number of the line buffers requiredin the on-vehicle image processing apparatus M100 of an embodiment ofthe present invention is 3. Here, in one line buffer, the number ofnecessary flip-flops is increased correspondingly to the image size(lateral width) in 1 frame. Thus, in the case of the image size of1000*1000 [pixel] per 1 frame, this results in the reduction in thenumber of 3000 flip-flops.

Merely reducing the number of the space filter processing circuits mayresult in the decrease in the precision of the image processing.However, according to the on-vehicle image processing apparatus M100 ofan embodiment of the present invention, the compensation of a lackingedge and the noise reduction can be performed at a same time. Thus, theimage of the high precision in which a lack of an edge of a lanedivision line is compensated and noise is reduced can be obtained, sothat a feature point on the road can be detected in which erroneousoperations are suppressed, similarly to the referential example of theon-vehicle image processing apparatus.

Moreover, in the referential on-vehicle image processing apparatus (inparticular, an apparatus for detecting an obstacle on a lane), after theexecution of the two kinds of one-dimensional space filtering processes,namely, the horizontal and the vertical edge detection filteringprocesses, in accordance with the execution result, a slant line isdetected by a slant line edge detecting process in an obstacle judgingmeans. Thus, in the referential example, a circuit for attaining twokinds of one-dimensional space filtering processes and the slant linedetecting process is required. Hence, there is a problem that thecircuit scale is increased.

On the other hand, in the on-vehicle image processing apparatus M100 ofan embodiment of the present invention, the slant line edge detectingprocess is not required, and the number of the processes and the circuitscale can be reduced.

[Effect]

As mentioned above, according an on-vehicle image processing apparatusM100 according to embodiments of the present invention, the scanningregions dedicated to the kinds of feature points (the lane divisionline, the branch, the dead end and the obstacle) on the road is set forthe feature point detection image data. Thus, the number of theinformation to be processed when the scanning is carried out can bereduced.

In an on-vehicle image processing apparatus M100 according to anembodiment of the present invention, for the pixel in the left or righthalf region in which a lane division line feature point cannot bedetected, the central pixel of the half region is complemented as thecomplemented lane division line feature point. Thus, the vehicleposition that is currently estimated can be made closer to the truevehicle position.

According to an on-vehicle image processing apparatus M100 according toan embodiment of the present invention, the number of the circuits forattaining the filtering process for the edge detection can be reduced sothat the circuit scale can be reduced.

Although the present invention has been described above in connectionwith several embodiments thereof, it would be apparent to those skilledin the art that those embodiments are provided solely for illustratingthe present invention, and should not be relied upon to construe theappended claims in a limiting sense.

What is claimed is:
 1. An on-vehicle image processing apparatuscomprising: an image taking apparatus configured to generate image databy taking an image of a forward view of a vehicle; an edge detectionsection configured to generate a detection image data including pixelsof m rows and n columns (m and n are integers more than 2) based on theimage data; and a feature point detection section configured to detectat least one feature point based on the detection image data and outputthe at least one feature point to an output apparatus, the feature pointdetection section categorizes the at least one feature point into a lanedivision line feature point indicating a point on a lane division line,a branch feature point indicating a branch of a lane, and a dead end andobstacle feature point indicating a dead end of a lane or an obstacle ona lane, the detection image data includes a lane division line detectionscanning region set for detecting the lane division line feature point,a branch detection scanning region set for detecting the branch featurepoint, and a dead end and obstacle detection scanning region set fordetecting the dead end and obstacle feature point, a distance between avehicle loading the image taking apparatus and a position on a lanecorresponding to a pixel in the detection image data becomes shorterfrom a first row to a m-th row of the detection image data, and the deadend and obstacle detection scanning region is set in pixels positionedfrom the first row to a m1-th row of the detection image data, thebranch detection scanning region is set in pixels positioned from them1-th row to a m2-th row of the detection image data, and the lanedivision line detection scanning region is set in pixels positioned fromthe m2-th row to the m-th row of the detection image data, wherein1<m1<m2<m.
 2. The on-vehicle image processing apparatus according toclaim 1, wherein the feature point detection section includes: a lanedivision line detection section configured to detect the lane divisionline by scanning the lane division line detection scanning region; abranch detection section configured to detect the branch feature pointby scanning the branch detection scanning region; and a dead end andobstacle detection section configured to detect the dead end andobstacle feature point by scanning the dead end and obstacle detectionscanning region.
 3. The on-vehicle image processing apparatus accordingto claim 2, wherein the lane division line detection section isconfigured to judge the lane division line feature point positioned in aleft region of the detection image data as a left side lane divisionline feature point, and judge the lane division line feature pointpositioned in a right region of the detection image data as a right sidelane division line feature point, and the feature point detectionsection further includes: a vehicle position estimation sectionconfigured to estimate a position of a vehicle loading the image takingapparatus based on the left side lane division line feature point andthe right side lane division line feature point.
 4. The on-vehicle imageprocessing apparatus according to claim 3, wherein the lane divisionline detection section is configured to scan pixels in the lane divisionline detection scanning region by one row at a time, and when one of theleft side lane division line feature point and the right side lanedivision line feature point on a certain row is detected and another oneof the left side lane division line feature point and the right sidelane division line feature point on the certain row is not detected,define a left or right half region in which the lane division linefeature point is not detected as a feature point lacking half region,and generate a complemented lane division line feature point positionedin a center of the feature point lacking half region in a scanningdirection, and the vehicle position estimation section is configured toestimate a position of a vehicle loading the image taking apparatusbased on a left side or right side lane division line feature pointdetected on the certain row and the complemented lane division linefeature point.
 5. The on-vehicle image processing apparatus according toclaim 1, wherein the edge detection section is configured to: calculatea weighted average of an intensity of a focused pixel and intensities ofsurrounding pixels surrounding the focused pixel by taking each of thepixels of m rows and n columns as the focused pixel; perform atwo-dimensional space filtering by generating a filtered image dataincluding filtered pixels, each of the filtered pixels has an intensitydetermined based on the weighted average; and generating a binarizedfiltered image data by binarizing each of pixels in the filtered imagedata by using a threshold intensity value.
 6. The on-vehicle imageprocessing apparatus according to claim 5, wherein the edge detectionsection is configured to perform the two-dimensional space filtering byusing a smoothing filter wherein the intensity of the focused pixel andthe intensities of surrounding pixels are equal, or a laplacian filter.7. The on-vehicle image processing apparatus according to claim 5,wherein each of the pixels in the binarized filtered image data has afirst pixel value or a second pixel value, and the lane division linedetection section is configured to detect a pixel having the firstintensity as a candidate lane division line feature point, and judge thecandidate lane division line feature point as the lane division linefeature point when the candidate lane division line feature point ismatched with a preset lane division line feature point pattern.
 8. Theon-vehicle image processing apparatus according to claim 5, wherein eachof the pixels in the binarized filtered image data has a first pixelvalue or a second pixel value, and the branch detection section isconfigured to detect a pixel having the first intensity as a candidatebranch feature point, and judge the candidate branch feature point asthe branch feature point when the candidate branch feature point ismatched with a preset branch feature point pattern.
 9. The on-vehicleimage processing apparatus according to claim 5, each of the pixels inthe binarized filtered image data has a first pixel value or a secondpixel value, and the dead end and obstacle detection section isconfigured to detect a pixel having the first intensity as a candidatedead end and obstacle feature point, and judges the candidate dead endand obstacle feature point as the dead end and obstacle feature pointwhen the candidate dead end and obstacle feature point is matched with apreset dead end and obstacle feature point pattern.
 10. The on-vehicleimage processing apparatus according to claim 5, wherein each of pixelsof the binarized filtered image data has a binary value of 0 or 1, andthe on-vehicle image processing apparatus further comprises an edgecorrection and noise reduction section configured to: count value 0pixel having the binary value of 0 and value 1 pixel having the binaryvalue of 1 among a focused binary pixel and surrounding binary pixelssurrounding the focused binary pixel, by taking each of the pixels ofthe binarized filtered image data as a focused binary pixel; generate afeature point detection image data by performing an edge correction andnoise reduction process in which the binary value is replaced by a newbinary value, wherein the new binary value is determined by a proportionof the counted number of the value 0 pixel to the counted number of thevalue 1 pixel, and a predetermined condition of an arrangement of thevalue 0 pixel and the value 1 pixel of the surrounding binary pixels foreach of the focused binary pixel; and output the feature point detectionimage data to the feature point detection section as the detection imagedata.
 11. The on-vehicle image processing apparatus according to claim1, further comprising: a memory; and A/D converter configured to convertthe image data generated by the image taking apparatus which is ananalogue data into a digital data and stores the digital data in thememory, wherein the edge detection section is configured to generate thedetection image data from the digital data stored in the memory.
 12. Theon-vehicle image processing apparatus according to claim 1, wherein theoutput apparatus is an automatic driving system for controlling a motionof a vehicle loading the image taking apparatus in response to the atleast one feature point.
 13. An image processing apparatus comprising:an edge detection section configured to generate a detection image dataincluding pixels of m rows and n columns (m and n are integers more than2) by applying an image processing to an image data which is an image ofa forward view of a vehicle; a feature point detection sectionconfigured to detect at least one feature point based on the detectionimage data and output the at least one feature point to an outputapparatus, the feature point detection section categorizes the at leastone feature point into a lane division line feature point indicating apoint on a lane division line, a branch feature point indicating abranch of a lane, and a dead end and obstacle feature point indicating adead end of a lane or an obstacle on a lane, the detection image dataincludes a lane division line detection scanning region set fordetecting the lane division line feature point, a branch detectionscanning region set for detecting the branch feature point, and a deadend and obstacle detection scanning region set for detecting the deadend and obstacle feature point, a distance between a vehicle loading theimage taking apparatus and a position on a lane corresponding to a pixelin the detection image data becomes shorter from a first row to a m-throw of the detection image data, and the dead end and obstacle detectionscanning region is set in pixels positioned from the first row to am1-th row of the detection image data, the branch detection scanningregion is set in pixels positioned from the m1-th row to a m2-th row ofthe detection image data, and the lane division line detection scanningregion is set in pixels positioned from the m2-th row to the m-th row ofthe detection image data, wherein 1<m1<m2<m.